#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size RANK_SIZE
# 网络名称，同目录名称

Network="TResNet"
# 训练batch_size

batch_size=190
# 训练使用的npu卡数

export RANK_SIZE=1

data_path_info=$1
data_path=`echo ${data_path_info#*=}`
# 校验是否传入data_path,不需要修改

if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

pth_path_info=$2
pth_path=`echo ${pth_path_info#*=}`
if [[ $pth_path == "" ]];then
    echo "[Error] para \"pth_path\" must be confing"
    exit 1
fi

###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径

cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi

#################创建日志输出目录，不需要修改#################

ASCEND_DEVICE_ID=0
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi


#################启动训练脚本#################
#训练开始时间，不需要修改

start_time=$(date +%s)
# 非平台场景时source 环境变量

check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi

nohup python validate.py ${data_path} -b=190 --model=tresnet_m --checkpoint=${pth_path} \
        >${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &

wait


##################获取训练数据################
# 训练结束时间，不需要修改

end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

# 结果打印，不需要修改

echo "------------------ Final result ------------------"

# 打印，不需要修改

echo "Final Performance images/sec : $FPS"

# 输出训练精度,需要模型审视修改

top1_err=`grep 'Acc@1' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'|awk 'END {print}'`
top1_err=`echo ${top1_err%,*}`
train_accuracy=$(echo "${top1_err}"|bc)
# 打印，不需要修改

echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

# 性能看护结果汇总
# 训练用例信息，不需要修改

BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'eval'

# 获取性能数据，不需要修改
# 吞吐量

ActualFPS=${FPS}
# 单迭代训练时长

TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`


# 关键信息打印到${CaseName}.log中，不需要修改

echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log